The package implements an integrated editing and imputation for continuous microdata under linear constraints. It relies on a Bayesian nonparametric hierarchical modeling approach in which the joint distribution of the data is estimated by a flexible joint probability model. The generated edit-imputed data are guaranteed to satisfy all imposed edit rules, whose types include ratio edits, balance edits and range restrictions.
|License:||GPL (>= 3)|
Quanli Wang, Hang J. Kim, Jerome P. Reiter, Lawrence H. Cox and Alan F. Karr
Maintainer: Quanli Wang <email@example.com> and Hang J. Kim <firstname.lastname@example.org>
Hang J. Kim, Lawrence H. Cox, Alan F. Karr, Jerome P. Reiter and Quanli Wang (2015). "Simultaneous Edit-Imputation for Continuous Microdata", Journal of the American Statistical Association, DOI: 10.1080/01621459.2015.1040881.
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library(EditImputeCont) ## read the toy example data, which has two ratio edits and a balance edit data(SimpleEx) data1 = readData(Y.original=SimpleEx$D.obs, ratio=SimpleEx$Ratio.edit, range=NULL, balance=SimpleEx$Balance.edit) ## create and initialize the model with 15 DP mixture components model1 = createModel(data.obj=data1, K=15) ## Run an iteration of MCMC # model1$Iterate() # dim(model1$Y.edited) ##  1000 4 # Edit-imputed datasets of n=1000 records with p=4 variables ## Please see the example in the demo folder for more detailed explanation
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